mindvideo.loss¶
GatherFeature¶
class mindvideo.loss.GatherFeature()
Gather feature at specified position.
base: nn.Cell
Parameters:
None
Return:
Tensor, feature at spectified position
TransposeGatherFeature¶
class mindvideo.loss.TransposeGatherFeature()
Transpose and gather feature at specified position
base: nn.Cell
Parameters:
None
Return:
Tensor, feature at spectified position
RegLoss¶
class mindvideo.loss.RegLoss(mode=’l1’)
Warpper for regression loss.
base: nn.Cell
Parameters:
mode(str): L1 or Smoothed L1 loss. Default: “l1”
Return:
Tensor, regression loss.
CenterNetMultiPoseLoss¶
class mindvideo.loss.CenterNetMultiPoseLoss(reg_loss, hm_weight, wh_weight, off_weight, reg_offset, reid_dim, nid, batch_size)
Warpper for regression loss.
base: nn.Cell
Parameters:
reg_loss (str): Regression loss, it can be L1 loss or Smooth L1 loss: ([’l1’, ‘sl1’]). Default=’l1’.
hm_weight (int): Loss weight for keypoint heatmaps. Default=1.
wh_weight (int): Loss weight for bounding box size. Default=0.1.
off_weight (int): Loss weight for keypoint local offsets. Default=1.
reg_offset (bool): Whether to use regress local offset. Default=True.
reid_dim (int): Feature embed dim. Default=128.
nID (int): Totoal number of identities in dataset. Default=14455.
batch_size (int): Number of imgs.
Return:
Tensor, total loss.
FocalLoss¶
class mindvideo.loss.FocalLoss(alpha=2, beta=4)
nn.Cell warpper for focal loss.
base: nn.Cell
Parameters:
alpha(int): Super parameter in focal loss to mimic loss weight. Default: 2.
beta(int): Super parameter in focal loss to mimic imbalance between positive and negative samples. Default: 4.
Return:
Tensor, focal loss.
DiceLoss¶
class mindvideo.loss.DiceLoss()
Compute the DICE loss, similar to generalized IOU for masks
base: nn.Cell
Parameters:
None
Return:
Tensor, DICE loss
SetCriterion¶
class mindvideo.loss.SetCriterion(num_classes, matcher, weight_dict, eos_coef, aux_loss)
vistr loss contains loss_labels, loss_masks and loss_boxes.
base: nn.LossBase
Parameters:
num_classes(int): Types of segmented objects.
matcher(cell): Match predictions to GT.
weight_dict(dict): Weights for different losses.
eos_coef(float): Background class weights.
aux_loss(bool): wether or not to computer aux loss.
Return:
Tensor, vistr loss
SigmoidFocalLoss¶
class mindvideo.loss.SigmoidFocalLoss()
Compute the sigmoid focal loss.
base: nn.Cell
Parameters:
alpha(float):Default: 0.25.
gamma(float):Default: 2.
Return:
Tensor, sigmoid focal loss